Overview

Dataset statistics

Number of variables11
Number of observations748
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.1 KiB
Average record size in memory287.6 B

Variable types

Categorical4
Numeric7

Warnings

Volumen (miles de kg) is highly correlated with Valor (miles de €)High correlation
Valor (miles de €) is highly correlated with Volumen (miles de kg)High correlation
Consumo per capita is highly correlated with Gasto per capitaHigh correlation
Gasto per capita is highly correlated with Consumo per capitaHigh correlation
Fecha is highly correlated with AñoHigh correlation
Año is highly correlated with FechaHigh correlation
Fecha is uniformly distributed Uniform
CCAA is uniformly distributed Uniform
Producto is uniformly distributed Uniform
Volumen (miles de kg) has unique values Unique
Valor (miles de €) has unique values Unique

Reproduction

Analysis started2021-04-14 06:38:32.406968
Analysis finished2021-04-14 06:38:38.597896
Duration6.19 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Fecha
Categorical

HIGH CORRELATION
UNIFORM

Distinct22
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
2018-07
 
34
2019-05
 
34
2018-10
 
34
2018-09
 
34
2019-11
 
34
Other values (17)
578 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5236
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-03
2nd row2018-03
3rd row2018-03
4th row2018-03
5th row2018-03
ValueCountFrequency (%)
2018-0734
 
4.5%
2019-0534
 
4.5%
2018-1034
 
4.5%
2018-0934
 
4.5%
2019-1134
 
4.5%
2019-0634
 
4.5%
2018-0634
 
4.5%
2018-0834
 
4.5%
2018-0534
 
4.5%
2020-0634
 
4.5%
Other values (12)408
54.5%
2021-04-14T08:38:38.794682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-0734
 
4.5%
2019-0534
 
4.5%
2018-1034
 
4.5%
2018-0934
 
4.5%
2019-1134
 
4.5%
2019-0634
 
4.5%
2018-0634
 
4.5%
2018-0834
 
4.5%
2018-0534
 
4.5%
2020-0634
 
4.5%
Other values (12)408
54.5%

Most occurring characters

ValueCountFrequency (%)
01564
29.9%
2884
16.9%
1816
15.6%
-748
14.3%
8374
 
7.1%
9374
 
7.1%
3102
 
1.9%
4102
 
1.9%
5102
 
1.9%
6102
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4488
85.7%
Dash Punctuation748
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
01564
34.8%
2884
19.7%
1816
18.2%
8374
 
8.3%
9374
 
8.3%
3102
 
2.3%
4102
 
2.3%
5102
 
2.3%
6102
 
2.3%
768
 
1.5%
ValueCountFrequency (%)
-748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5236
100.0%

Most frequent character per script

ValueCountFrequency (%)
01564
29.9%
2884
16.9%
1816
15.6%
-748
14.3%
8374
 
7.1%
9374
 
7.1%
3102
 
1.9%
4102
 
1.9%
5102
 
1.9%
6102
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5236
100.0%

Most frequent character per block

ValueCountFrequency (%)
01564
29.9%
2884
16.9%
1816
15.6%
-748
14.3%
8374
 
7.1%
9374
 
7.1%
3102
 
1.9%
4102
 
1.9%
5102
 
1.9%
6102
 
1.9%

Año
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2019
306 
2018
306 
2020
136 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2992
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018
ValueCountFrequency (%)
2019306
40.9%
2018306
40.9%
2020136
18.2%
2021-04-14T08:38:39.050488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T08:38:39.130570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2018306
40.9%
2019306
40.9%
2020136
18.2%

Most occurring characters

ValueCountFrequency (%)
2884
29.5%
0884
29.5%
1612
20.5%
8306
 
10.2%
9306
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2992
100.0%

Most frequent character per category

ValueCountFrequency (%)
2884
29.5%
0884
29.5%
1612
20.5%
8306
 
10.2%
9306
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common2992
100.0%

Most frequent character per script

ValueCountFrequency (%)
2884
29.5%
0884
29.5%
1612
20.5%
8306
 
10.2%
9306
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2992
100.0%

Most frequent character per block

ValueCountFrequency (%)
2884
29.5%
0884
29.5%
1612
20.5%
8306
 
10.2%
9306
 
10.2%

Mes
Real number (ℝ≥0)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.545454545
Minimum3
Maximum11
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:39.214803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median6
Q39
95-th percentile11
Maximum11
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.573017893
Coefficient of variation (CV)0.3930999559
Kurtosis-1.157823685
Mean6.545454545
Median Absolute Deviation (MAD)2
Skewness0.2690193841
Sum4896
Variance6.620421078
MonotocityNot monotonic
2021-04-14T08:38:39.323328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6102
13.6%
5102
13.6%
4102
13.6%
3102
13.6%
1168
9.1%
1068
9.1%
968
9.1%
868
9.1%
768
9.1%
ValueCountFrequency (%)
3102
13.6%
4102
13.6%
5102
13.6%
6102
13.6%
768
9.1%
868
9.1%
968
9.1%
1068
9.1%
1168
9.1%
ValueCountFrequency (%)
1168
9.1%
1068
9.1%
968
9.1%
868
9.1%
768
9.1%
6102
13.6%
5102
13.6%
4102
13.6%
3102
13.6%

CCAA
Categorical

UNIFORM

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
Castilla-La Mancha
 
44
Aragón
 
44
Canarias
 
44
La Rioja
 
44
Castilla y León
 
44
Other values (12)
528 

Length

Max length26
Median length13
Mean length13.88235294
Min length6

Characters and Unicode

Total characters10384
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndalucía
2nd rowAragón
3rd rowPrincipado de Asturias
4th rowIlles Balears
5th rowCanarias
ValueCountFrequency (%)
Castilla-La Mancha44
 
5.9%
Aragón44
 
5.9%
Canarias44
 
5.9%
La Rioja44
 
5.9%
Castilla y León44
 
5.9%
Cantabria44
 
5.9%
Comunitat Valenciana44
 
5.9%
Galicia44
 
5.9%
Región de Murcia44
 
5.9%
Principado de Asturias44
 
5.9%
Other values (7)308
41.2%
2021-04-14T08:38:39.721948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de176
 
12.1%
comunidad88
 
6.1%
comunitat44
 
3.0%
murcia44
 
3.0%
vasco44
 
3.0%
galicia44
 
3.0%
castilla44
 
3.0%
cataluña\/catalunya44
 
3.0%
la44
 
3.0%
illes44
 
3.0%
Other values (19)836
57.6%

Most occurring characters

ValueCountFrequency (%)
a2024
19.5%
i748
 
7.2%
704
 
6.8%
n616
 
5.9%
d572
 
5.5%
l572
 
5.5%
r572
 
5.5%
e440
 
4.2%
u396
 
3.8%
s396
 
3.8%
Other values (31)3344
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8228
79.2%
Uppercase Letter1320
 
12.7%
Space Separator704
 
6.8%
Other Punctuation88
 
0.8%
Dash Punctuation44
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
a2024
24.6%
i748
 
9.1%
n616
 
7.5%
d572
 
7.0%
l572
 
7.0%
r572
 
7.0%
e440
 
5.3%
u396
 
4.8%
s396
 
4.8%
t396
 
4.8%
Other values (14)1496
18.2%
ValueCountFrequency (%)
C396
30.0%
A132
 
10.0%
L132
 
10.0%
M132
 
10.0%
P88
 
6.7%
R88
 
6.7%
V88
 
6.7%
I44
 
3.3%
B44
 
3.3%
E44
 
3.3%
Other values (3)132
 
10.0%
ValueCountFrequency (%)
\44
50.0%
/44
50.0%
ValueCountFrequency (%)
704
100.0%
ValueCountFrequency (%)
-44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9548
91.9%
Common836
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
a2024
21.2%
i748
 
7.8%
n616
 
6.5%
d572
 
6.0%
l572
 
6.0%
r572
 
6.0%
e440
 
4.6%
u396
 
4.1%
s396
 
4.1%
t396
 
4.1%
Other values (27)2816
29.5%
ValueCountFrequency (%)
704
84.2%
-44
 
5.3%
\44
 
5.3%
/44
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10120
97.5%
None264
 
2.5%

Most frequent character per block

ValueCountFrequency (%)
a2024
20.0%
i748
 
7.4%
704
 
7.0%
n616
 
6.1%
d572
 
5.7%
l572
 
5.7%
r572
 
5.7%
e440
 
4.3%
u396
 
3.9%
s396
 
3.9%
Other values (28)3080
30.4%
ValueCountFrequency (%)
ó132
50.0%
í88
33.3%
ñ44
 
16.7%

Producto
Categorical

UNIFORM

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
T.HORTALIZAS FRESCAS
374 
T.FRUTAS FRESCAS
374 

Length

Max length20
Median length18
Mean length18
Min length16

Characters and Unicode

Total characters13464
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT.HORTALIZAS FRESCAS
2nd rowT.HORTALIZAS FRESCAS
3rd rowT.HORTALIZAS FRESCAS
4th rowT.HORTALIZAS FRESCAS
5th rowT.HORTALIZAS FRESCAS
ValueCountFrequency (%)
T.HORTALIZAS FRESCAS374
50.0%
T.FRUTAS FRESCAS374
50.0%
2021-04-14T08:38:40.006425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T08:38:40.099655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
frescas748
50.0%
t.frutas374
25.0%
t.hortalizas374
25.0%

Most occurring characters

ValueCountFrequency (%)
S2244
16.7%
A1870
13.9%
T1496
11.1%
R1496
11.1%
F1122
8.3%
.748
 
5.6%
748
 
5.6%
E748
 
5.6%
C748
 
5.6%
H374
 
2.8%
Other values (5)1870
13.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11968
88.9%
Other Punctuation748
 
5.6%
Space Separator748
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
S2244
18.8%
A1870
15.6%
T1496
12.5%
R1496
12.5%
F1122
9.4%
E748
 
6.2%
C748
 
6.2%
H374
 
3.1%
O374
 
3.1%
L374
 
3.1%
Other values (3)1122
9.4%
ValueCountFrequency (%)
.748
100.0%
ValueCountFrequency (%)
748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11968
88.9%
Common1496
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
S2244
18.8%
A1870
15.6%
T1496
12.5%
R1496
12.5%
F1122
9.4%
E748
 
6.2%
C748
 
6.2%
H374
 
3.1%
O374
 
3.1%
L374
 
3.1%
Other values (3)1122
9.4%
ValueCountFrequency (%)
.748
50.0%
748
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13464
100.0%

Most frequent character per block

ValueCountFrequency (%)
S2244
16.7%
A1870
13.9%
T1496
11.1%
R1496
11.1%
F1122
8.3%
.748
 
5.6%
748
 
5.6%
E748
 
5.6%
C748
 
5.6%
H374
 
2.8%
Other values (5)1870
13.9%

Volumen (miles de kg)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct748
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17775.62045
Minimum965.63
Maximum74537.73
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:40.194719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum965.63
5-th percentile2129.3885
Q16456.08
median10615.795
Q324196.0375
95-th percentile56062.028
Maximum74537.73
Range73572.1
Interquartile range (IQR)17739.9575

Descriptive statistics

Standard deviation16676.60417
Coefficient of variation (CV)0.9381728314
Kurtosis1.235871105
Mean17775.62045
Median Absolute Deviation (MAD)6314.4
Skewness1.438292125
Sum13296164.1
Variance278109126.7
MonotocityNot monotonic
2021-04-14T08:38:40.359915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27674.861
 
0.1%
34050.871
 
0.1%
10900.841
 
0.1%
41835.071
 
0.1%
24676.781
 
0.1%
9433.731
 
0.1%
56467.111
 
0.1%
10759.111
 
0.1%
10581.51
 
0.1%
2280.091
 
0.1%
Other values (738)738
98.7%
ValueCountFrequency (%)
965.631
0.1%
11121
0.1%
1119.71
0.1%
1162.951
0.1%
1181.041
0.1%
1228.541
0.1%
1235.791
0.1%
1333.461
0.1%
1333.781
0.1%
1383.151
0.1%
ValueCountFrequency (%)
74537.731
0.1%
71883.851
0.1%
71869.971
0.1%
71261.381
0.1%
69570.321
0.1%
69299.621
0.1%
690841
0.1%
68945.411
0.1%
68311.031
0.1%
68180.691
0.1%

Valor (miles de €)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct748
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30127.16766
Minimum1752.05
Maximum152351.12
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:40.530748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1752.05
5-th percentile3876.7035
Q111580.1675
median18621.025
Q342810.8075
95-th percentile89290.8045
Maximum152351.12
Range150599.07
Interquartile range (IQR)31230.64

Descriptive statistics

Standard deviation27942.9279
Coefficient of variation (CV)0.9274993325
Kurtosis1.505808811
Mean30127.16766
Median Absolute Deviation (MAD)10961.045
Skewness1.463166068
Sum22535121.41
Variance780807219.4
MonotocityNot monotonic
2021-04-14T08:38:40.687024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6336.31
 
0.1%
3919.471
 
0.1%
23184.391
 
0.1%
88184.011
 
0.1%
30056.111
 
0.1%
70794.271
 
0.1%
80538.851
 
0.1%
8694.11
 
0.1%
5090.151
 
0.1%
6706.171
 
0.1%
Other values (738)738
98.7%
ValueCountFrequency (%)
1752.051
0.1%
2142.061
0.1%
2257.541
0.1%
2407.061
0.1%
2454.11
0.1%
2491.41
0.1%
2581.081
0.1%
2610.991
0.1%
2720.221
0.1%
2785.611
0.1%
ValueCountFrequency (%)
152351.121
0.1%
136585.141
0.1%
134417.361
0.1%
125592.171
0.1%
120584.521
0.1%
119607.841
0.1%
117787.651
0.1%
116167.341
0.1%
114096.111
0.1%
113798.921
0.1%

Precio medio kg
Real number (ℝ≥0)

Distinct99
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.732954545
Minimum1.14
Maximum2.23
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:40.859903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile1.39
Q11.59
median1.74
Q31.89
95-th percentile2.04
Maximum2.23
Range1.09
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2030311483
Coefficient of variation (CV)0.1171589577
Kurtosis-0.3733476205
Mean1.732954545
Median Absolute Deviation (MAD)0.15
Skewness-0.2139075562
Sum1296.25
Variance0.04122164719
MonotocityNot monotonic
2021-04-14T08:38:41.032083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7218
 
2.4%
1.918
 
2.4%
1.8717
 
2.3%
1.8617
 
2.3%
1.8117
 
2.3%
1.5417
 
2.3%
1.6116
 
2.1%
1.6816
 
2.1%
1.6716
 
2.1%
1.7415
 
2.0%
Other values (89)581
77.7%
ValueCountFrequency (%)
1.141
 
0.1%
1.151
 
0.1%
1.21
 
0.1%
1.212
0.3%
1.231
 
0.1%
1.243
0.4%
1.261
 
0.1%
1.282
0.3%
1.293
0.4%
1.33
0.4%
ValueCountFrequency (%)
2.231
 
0.1%
2.211
 
0.1%
2.21
 
0.1%
2.183
0.4%
2.161
 
0.1%
2.151
 
0.1%
2.143
0.4%
2.134
0.5%
2.122
0.3%
2.111
 
0.1%

Penetración (%)
Real number (ℝ≥0)

Distinct488
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.64737968
Minimum83.7
Maximum99.93
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:41.199837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum83.7
5-th percentile90.8835
Q194.57
median96.215
Q397.365
95-th percentile98.6265
Maximum99.93
Range16.23
Interquartile range (IQR)2.795

Descriptive statistics

Standard deviation2.47322656
Coefficient of variation (CV)0.02585775552
Kurtosis2.476143849
Mean95.64737968
Median Absolute Deviation (MAD)1.335
Skewness-1.349754378
Sum71544.24
Variance6.116849617
MonotocityNot monotonic
2021-04-14T08:38:41.366370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.46
 
0.8%
97.895
 
0.7%
97.45
 
0.7%
97.444
 
0.5%
95.964
 
0.5%
96.094
 
0.5%
96.534
 
0.5%
95.814
 
0.5%
98.024
 
0.5%
96.734
 
0.5%
Other values (478)704
94.1%
ValueCountFrequency (%)
83.71
0.1%
84.971
0.1%
85.181
0.1%
85.761
0.1%
85.941
0.1%
86.341
0.1%
87.011
0.1%
87.041
0.1%
87.121
0.1%
87.311
0.1%
ValueCountFrequency (%)
99.931
0.1%
99.761
0.1%
99.751
0.1%
99.561
0.1%
99.551
0.1%
99.431
0.1%
99.411
0.1%
99.361
0.1%
99.211
0.1%
99.21
0.1%

Consumo per capita
Real number (ℝ≥0)

HIGH CORRELATION

Distinct466
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.669251337
Minimum2.78
Maximum14.29
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:41.533459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.78
5-th percentile3.81
Q14.8875
median6.475
Q38.2025
95-th percentile10.2665
Maximum14.29
Range11.51
Interquartile range (IQR)3.315

Descriptive statistics

Standard deviation2.071850598
Coefficient of variation (CV)0.3106571478
Kurtosis-0.600069684
Mean6.669251337
Median Absolute Deviation (MAD)1.65
Skewness0.3977149357
Sum4988.6
Variance4.292564901
MonotocityNot monotonic
2021-04-14T08:38:41.706384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.527
 
0.9%
7.177
 
0.9%
4.974
 
0.5%
8.124
 
0.5%
3.854
 
0.5%
4.484
 
0.5%
8.034
 
0.5%
5.564
 
0.5%
5.354
 
0.5%
5.14
 
0.5%
Other values (456)702
93.9%
ValueCountFrequency (%)
2.781
0.1%
3.051
0.1%
3.161
0.1%
3.251
0.1%
3.352
0.3%
3.371
0.1%
3.381
0.1%
3.412
0.3%
3.431
0.1%
3.462
0.3%
ValueCountFrequency (%)
14.291
0.1%
12.861
0.1%
12.311
0.1%
11.891
0.1%
11.71
0.1%
11.561
0.1%
11.511
0.1%
11.381
0.1%
11.261
0.1%
11.21
0.1%

Gasto per capita
Real number (ℝ≥0)

HIGH CORRELATION

Distinct557
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.37536096
Minimum5.54
Maximum27.63
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-04-14T08:38:41.929901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.54
5-th percentile6.9235
Q18.935
median10.935
Q313.2525
95-th percentile17.2995
Maximum27.63
Range22.09
Interquartile range (IQR)4.3175

Descriptive statistics

Standard deviation3.324742988
Coefficient of variation (CV)0.292275823
Kurtosis1.154433315
Mean11.37536096
Median Absolute Deviation (MAD)2.16
Skewness0.9066751329
Sum8508.77
Variance11.05391593
MonotocityNot monotonic
2021-04-14T08:38:42.128088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.764
 
0.5%
11.014
 
0.5%
12.024
 
0.5%
10.554
 
0.5%
11.383
 
0.4%
10.173
 
0.4%
12.43
 
0.4%
12.043
 
0.4%
13.93
 
0.4%
12.23
 
0.4%
Other values (547)714
95.5%
ValueCountFrequency (%)
5.541
0.1%
5.611
0.1%
5.661
0.1%
5.691
0.1%
5.721
0.1%
5.761
0.1%
5.91
0.1%
6.211
0.1%
6.261
0.1%
6.361
0.1%
ValueCountFrequency (%)
27.631
0.1%
24.241
0.1%
23.071
0.1%
22.221
0.1%
22.051
0.1%
21.451
0.1%
21.391
0.1%
21.261
0.1%
20.981
0.1%
20.751
0.1%

Interactions

2021-04-14T08:38:33.038992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.208010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.335901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.494032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.684267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.810102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:33.959309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.156402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.273239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.383539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.494652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.604686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.708912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.826726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:34.944117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.053101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.172871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.273878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.385851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.488006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.596552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.699211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:35.975560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.077628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.176862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.286587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.405044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.514434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.627524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.737962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.843152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:36.948736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.049869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.157689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.252620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.360105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.452590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.555254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.657205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.765345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.861775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T08:38:37.963939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-14T08:38:42.263217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T08:38:42.413825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T08:38:42.718201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T08:38:42.868597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-14T08:38:43.012987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-14T08:38:38.129629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-14T08:38:38.493551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FechaAñoMesCCAAProductoVolumen (miles de kg)Valor (miles de €)Precio medio kgPenetración (%)Consumo per capitaGasto per capita
02018-0320183AndalucíaT.HORTALIZAS FRESCAS38505.7966399.931.7296.934.437.64
12018-0320183AragónT.HORTALIZAS FRESCAS7578.7813834.071.8398.115.7710.54
22018-0320183Principado de AsturiasT.HORTALIZAS FRESCAS3701.717008.991.8993.713.416.46
32018-0320183Illes BalearsT.HORTALIZAS FRESCAS5728.0310921.541.9195.815.5610.60
42018-0320183CanariasT.HORTALIZAS FRESCAS10900.8419963.161.8397.724.999.13
52018-0320183CantabriaT.HORTALIZAS FRESCAS1875.963166.491.6995.393.375.69
62018-0320183Castilla-La ManchaT.HORTALIZAS FRESCAS9127.2615770.061.7397.944.357.52
72018-0320183Castilla y LeónT.HORTALIZAS FRESCAS8873.4315699.991.7794.573.636.42
82018-0320183Cataluña\/CatalunyaT.HORTALIZAS FRESCAS44416.7081759.891.8497.766.4211.82
92018-0320183ExtremaduraT.HORTALIZAS FRESCAS4532.878213.681.8198.833.846.97

Last rows

FechaAñoMesCCAAProductoVolumen (miles de kg)Valor (miles de €)Precio medio kgPenetración (%)Consumo per capitaGasto per capita
7382020-0620206Castilla y LeónT.FRUTAS FRESCAS25494.9442843.781.6895.4711.1218.69
7392020-0620206Cataluña\/CatalunyaT.FRUTAS FRESCAS68945.41136585.141.9896.869.9819.77
7402020-0620206ExtremaduraT.FRUTAS FRESCAS9673.8916051.331.6696.938.6314.32
7412020-0620206GaliciaT.FRUTAS FRESCAS26235.7750627.471.9394.9810.1119.50
7422020-0620206La RiojaT.FRUTAS FRESCAS2419.764879.422.0299.037.8815.90
7432020-0620206Comunidad de MadridT.FRUTAS FRESCAS62836.45112799.101.8097.1610.3218.53
7442020-0620206Región de MurciaT.FRUTAS FRESCAS14095.6622926.481.6396.1610.1516.51
7452020-0620206Comunidad Foral de NavarraT.FRUTAS FRESCAS6188.8911851.161.9196.0310.7820.63
7462020-0620206País VascoT.FRUTAS FRESCAS22219.7343357.871.9597.8510.9621.39
7472020-0620206Comunitat ValencianaT.FRUTAS FRESCAS43709.9878115.911.7995.949.3816.77